SUMMARY Although solid tumors oftentimes arise from the uninhibited growth of a single aberrant cell, the population of cells within a tumor are genetically and molecularly heterogenous. This makes it very difficult to evaluate a therapy?s efficacy when using bulk population measurements. Furthermore, recent research has implicated microRNAs (miRNAs) as drivers of progression and malignancy in many tumor types. MiRNAs, small non- coding RNAs that are typically about 20 nucleotides long, can modulate target gene expression by binding to mRNA, signaling for its degradation or inactivation. The relationship between miRNA and target genes becomes even more complex since miRNAs can target multiple mRNAs, and most of what is known about miRNA targets is theoretical and based on sequence-based predication algorithms. It is the correlation between miRNAs and target protein that therapy developers need to be able to monitor to tell if their therapy is going to be effective for a certain tumor type. The only way to quickly discover accurate correlations between RNA and proteins, is to monitor the transcriptome and proteome on a single cell level. This will tell drug developers, exactly what the relationship is between a specific miRNA and protein of intertest, and in what cell types these correlations are most prevalent. The IsoPlexis Single-Cell Barcode (SCBC) is the only technology that currently exists that can obtain this correlation data from single cells, measuring up to 42 secreted or intracellular proteins along with up to 4 phenotypic surface markers from a single cell making our technology able to measure 10-fold more proteins per cell than our competitors and requires only a few thousand cells to provide significant, and importantly quantitative, results. Our platform has recently been further developed to lyse cells on-chip in a cell specific manner, allowing for the capture of circulating RNA species, including miRNA and mRNA. Herein, we propose to use this phase I grant to develop a new tool, using the IsoPlexis SCBC platform, for multi-omic analysis that can (i) make highly multiplexed measurements from a single cell (ii) while being able to gather both proteomic and transcriptomic data from the same cell (iii) in a manner that is high throughout and capable of being fully automated. We propose the following specific aims: Aim 1. Develop and optimize SCBC flow cell for dual capture of protein and RNA on-chip. Aim 2. Deliver validation for ability to monitor multi-omic GBM biomarkers from a single-cell using 10 patient samples in collaboration with UCLA and Caltech.